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EI: A Program for Ecological Inference

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  • King, Gary

Abstract

The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King'97). Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological") data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act) and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.

Suggested Citation

  • King, Gary, 2004. "EI: A Program for Ecological Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i07).
    • Handle: RePEc:jss:jstsof:v:011:i07
    DOI: http://hdl.handle.net/10.18637/jss.v011.i07
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    Cited by:

    1. Rob Eisinga, 2009. "The beta‐binomial convolution model for 2×2 tables with missing cell counts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 24-42, February.
    2. Roberto Colombi & Antonio Forcina, 2016. "Latent class models for ecological inference on voters transitions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 501-517, November.
    3. Lehmann, Sibylle & Volckart, Oliver, 2011. "The political economy of agricultural protection: Sweden 1887," European Review of Economic History, Cambridge University Press, vol. 15(1), pages 29-59, April.
    4. Joan G. Staniswalis, 2008. "Incorporating Marginal Covariate Information in a Nonparametric Regression Model for a Sample of R×C Tables," Biometrics, The International Biometric Society, vol. 64(4), pages 1054-1061, December.
    5. A. Forcina & M. Gnaldi & B. Bracalente, 2012. "A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 109-119, March.

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